Valorization of lignocellulosic biomass forest residues in quebec via the integrated hydropyrolysis and hydroconversion (IH2) technology: A review
Bibliographic record
Abstract
Lignocellulosic biomass residues from Quebec's forests, such as branches and logging residues, can be thermochemically converted into hydrocarbons-rich renewable fuels, offering a sustainable alternative to fossil oil in transportation. Integrated Hydropyrolysis and Hydroconversion (IH 2 ) has emerged as a promising biorefinery technology for transforming diverse biomass feedstocks into bio-oil with properties akin to petroleum, achieving approximately 45 % carbon conversion and 27 wt% bio-oil yield. Optimal conditions for catalytic fast hydropyrolysis (CFHP) include temperatures of 400–450 °C, hydrogen pressures of 20–30 bar, and biomass heating rates of 100–500 °C/min. For the catalytic hydroconversion (CHC) stage, lower temperatures of 250–400 °C with similar hydrogen pressures are beneficial. To enhance bio-oil quality and yield, feed particle sizes below 1 mm are recommended for enhanced heat transfer. While clean hydrogen for IH 2 could be produced from electrolysis and biomass, an alternative co-CFHP process using hydrogen-rich bio-based solvents, UEO, and waste plastics can mitigate the need for external hydrogen, high pressures, and complex setups. This can be operated with or without an optional hydrotreatment stage based on required product specifications. The results highlight some potential catalyst alternatives to TMS like TMO, TMP, TMN , and TMC that could improve deoxygenation efficiency and reduce associated challenges. Carbonaceous supports like biochars can replace conventional Al 2 O 3 and achieve better performance w.r.t coking, surface functionalities, metal dispersibility, and sensitivity to deoxygenation by-products. Additionally, fluidized bed reactors are suggested for their scalability and effectiveness in facilitating consistent reactions. Overall, this study underscores the viability of IH 2 and identifies critical areas for further research to achieve demonstration-scale feasibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".